This document describes the files included in the replication directory for Franzese, Hays, and Kachi "Modeling History Dependence in Network-Behavior Coevolution."


*There are two files that can be used to replicate the Monte Carlo results.

1) NETWORK_DGP_N50_T20_Rb5_Rn5_MC100.m
 
	This is a MATLAB file that a) generates network and behavior coevolution data using the Siena statisical 	model as the DGP, b) saves the generated data in files that can be imported into RSiena, and c) estimates the simple time-lagged spatial-lag logistic-regression model using the generated data. The main parameters of the experiments (i.e., number of actors, number of periods the networks and behavior are observed, the rate of change in behavior, and the rate of change in network ties) can be altered in this file.

2) FHK_MonteCarlos_RSiena.R
	
	This is an R script file that will take the coevolution data generated in MATLAB and estimate the parameters of the Siena model using simulated method-of-moments.


*The simple time-lagged spatial-lag logistic-regression models in the military alliances and conflict behavior illustration were estimated and can be replicated using STATA version 9.                                      

3) combined.dta

	This is a STATA data file that contains the alliance network and conflict behavior data for the great powers from 1900-1950.  

4) stata_commands_output.txt

	The file contains the STATA commands and resulting output.


*The Siena models in the military alliances and conflict behavior illustration were estimated and can be replicated using StOCNET Version 1.8 (Siena version 3.11).

5) 1900a.txt-1950a.txt

	These files (11 in total) contain the alliance network data for the great powers observed in five-year increments between 1900 and 1950.

6) behavior00_50_5s.txt

	This file that contains the conflict behavior data for the great powers observed in five-year increments between 1900 and 1950.

7) siena_options_output.txt

	This file contains the Siena options--random number seed, model type, initial parameter values, etc.--chosen to estimate the model and the resulting output.
